Conventional approaches to indicators and metrics undermine urban climate adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measurement is essential for effective adaptation management and operation, and indicators and metrics (I&M) have a pivotal role. Surprisingly, systematic efforts to assess advances in the provision of adaptation I&M are scarce, and those that do exist often lack in-depth analysis of the types, characteristics, and applicability of the collected information. Here, we analyse 137 publications and 901 I&M sourced in the scientific literature (2007-2022) to measure adaptation to climate change in urban areas where governments are increasingly placing efforts to prepare populations and infrastructures. A lack of common terminology, standardisation, and guidelines has resulted in a field that is complex to track and understand. This complexity has led to a fragmented methodological landscape, marked by diverse, context-dependent, and occasionally conflicting approaches to the development of I&M. We argue that conventional approaches to I&M are largely inadequate and must better emphasise quantifiability, long-term assessment, and alignment with policy objectives.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it